工业工程

工业工程 ›› 2022, Vol. 25 ›› Issue (5): 106-112.doi: 10.3969/j.issn.1007-7375.2022.05.013

• 专题论述 • 上一篇    下一篇

基于critic组合预测方法的共享单车故障预测

蔡芸, 杨江辉, 熊禾根   

  1. 武汉科技大学 冶金装备及其控制教育部重点实验室，机械传动与制造工程湖北省重点实验室，湖北 武汉 430081
  • 收稿日期:2021-05-12 发布日期:2022-10-20
  • 作者简介:蔡芸(1970—)，女，江苏省人，副教授，博士，主要研究方向为优化算法、生产调度
  • 基金资助:
    国家自然科学基金资助项目(51875422)

Fault Prediction of Sharing Bikes Based on Critic Combination Prediction Method

CAI Yun, YANG Jianghui, XIONG Hegen   

  1. Key Laboratory of Metallurgical Equipment and Control Technology, Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
  • Received:2021-05-12 Published:2022-10-20

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摘要： 针对无桩式共享单车系统缺少故障预测方法的研究及预测准确率低不稳定等问题，在挖掘预测因素和故障单车之间的信息特征上，提出改进熵权法对预测因素分权，并采用BP神经网络、径向基函数和ELMAN神经网络3种单一预测模型建立基于critic权重的组合预测模型，在Matlab上进行实例求解。结果表明，相比单一预测方法，该组合预测方法提高了预测准确率5%左右，且能够降低预测风险，减少预测的系统误差，具有较好的实用价值。

关键词: 共享单车, 组合预测, critic赋权, 改进熵权法

Abstract: In view of the lack of research on fault prediction method, low prediction accuracy and instability of dockless sharing bikes system, and on the basis of mining the information features between the predictive factors and the faulty bikes, an improved entropy weight method is proposed to divide the weights of the predictive factors. And three single prediction models, BP neural network, radial basis function and Elman neural network, are used to establish a combined prediction model based on critical weight, to solve an example on Matlab. The results show that: compared with the single prediction method, the combined prediction method improves the prediction accuracy by about 5%, and can reduce the prediction risk and system error and has good practical value.

Key words: sharing bikes, combination prediction, critical weighted, improved entropy weight method

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